From 9380ce139d0ef784390d72372642d8bea48ec30a Mon Sep 17 00:00:00 2001 From: Ted Yun Date: Mon, 23 Feb 2026 11:29:28 -0500 Subject: [PATCH] TRIAD-HFpEF paper open-source code first commit --- triad-hfpef/CONTRIBUTING.md | 29 ++++ triad-hfpef/LICENSE | 202 +++++++++++++++++++++++++ triad-hfpef/README.md | 26 ++++ triad-hfpef/model_checkpoint/README.md | 14 ++ triad-hfpef/models.py | 111 ++++++++++++++ 5 files changed, 382 insertions(+) create mode 100644 triad-hfpef/CONTRIBUTING.md create mode 100644 triad-hfpef/LICENSE create mode 100644 triad-hfpef/README.md create mode 100644 triad-hfpef/model_checkpoint/README.md create mode 100644 triad-hfpef/models.py diff --git a/triad-hfpef/CONTRIBUTING.md b/triad-hfpef/CONTRIBUTING.md new file mode 100644 index 0000000..e5c3b28 --- /dev/null +++ b/triad-hfpef/CONTRIBUTING.md @@ -0,0 +1,29 @@ +# How to Contribute + +We'd love to accept your patches and contributions to this project. 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All prediction targets must + be either binary or "probabilities" between 0 and 1. + main_target_weight: The weight of the main target, which is assumed to be + the first target. Ignored when target_count is 1. For example, if + target_count is 3 and main_target_weight is 0.5, then the loss is 0.5 * + loss_target_0 + 0.25 * loss_target_1 + 0.25 * loss_target_2. + pretrain_weights: Pretrain weights to use. 'imagenet' or None. + name: The name of the model. + + Returns: + A Keras ResNet50 V2 model for binary classification. + """ + if pretrain_weights and pretrain_weights != 'imagenet': + raise ValueError(f'Unsupported pretrain_weights: {pretrain_weights}') + if main_target_weight < 0.0 or main_target_weight > 1.0: + raise ValueError('Main target weight must be between 0 and 1.') + elif target_count == 1 and main_target_weight != 1.0: + raise ValueError( + 'Main target weight must be 1.0 when there is only one target.' + ) + elif target_count > 1 and main_target_weight == 1.0: + raise ValueError( + 'Main target weight must be <1 when there are multiple targets.' + ) + image_shape = [224, 224, 3] + image_inputs = tf.keras.Input( + shape=[image_count] + image_shape, name=f'{name}_input' + ) + label_inputs = [ + tf.keras.Input(shape=[1], name=f'{name}_label_{i}') + for i in range(target_count) + ] + x = image_inputs + shared_backbone = tf.keras.applications.ResNet50V2( + include_top=True, + weights=pretrain_weights, + ) + output_lists = [] + for i in range(image_count): + x_i = tf.gather(x, indices=i, axis=1, name=f'{name}_slice{i}') + output_lists.append(shared_backbone(x_i)) + + x = tf.keras.layers.Concatenate(name=f'{name}_concat', axis=-1)(output_lists) + outputs = [ + tf.keras.layers.Dense( + units=1, + activation='sigmoid', + name=f'{name}_pred_{i}', + )(x) + for i in range(target_count) + ] + assert len(outputs) == len(label_inputs) + model = tf.keras.Model( + inputs=[image_inputs] + label_inputs, outputs=outputs, name=name + ) + losses = [ + tf.keras.backend.binary_crossentropy( + label_inputs[i], + outputs[i], + from_logits=False, + ) + for i in range(target_count) + ] + if target_count == 1: + weights = [1.0] + else: + weights = [main_target_weight] + [ + (1 - main_target_weight) / (target_count - 1) + ] * (target_count - 1) + assert len(losses) == len(weights) + combined_loss = 0 + for i in range(target_count): + model.add_metric( + losses[i], + name=f'binary_crossentropy_{i}', + ) + combined_loss += weights[i] * losses[i] + model.add_loss(combined_loss) + return model